factors determining maritime transport market: time series

Volume 6, Issue 11, November – 2021 International Journal of Innovative Science and Research Technology

ISSN No:-2456-2165

IJISRT21NOV450 www.ijisrt.com 375

Factors Determining Maritime Transport Market:

Time Series Econometric Analysis on Container

Throughput of Cambodia’s Ports

KiriromCheav, PhD

Graduation School, National University of Management

Phnom Penh, Cambodia

Abstract:-The significantly increased demand for

container throughput and cargo traffic volume

transported in Cambodia has improved and accelerated

the port output advancement and the development of new

terminal. The main objective of this research is to

investigate the long-run and short-run relationships, as

well as to identify the influences on Cambodian combined

international ports’ container throughput.The researcher

collected the yearly data (1992 - 2017) from reliable and

official sources of PAS and PPAP ports, IMF, WB, and

MOC, and additionally interviewed with port authorities,

port operators, and qualified stakeholders. The

econometric approaches of The Unit Roots Test, Johansen

Test of Cointegration, VECM, and Test for Stability are

performed and applied in the analysis on the time series

data between container throughput as the dependent

variable and independent variables such as number of

ship call, export, import, and GDP per capita. The

estimation result confirmed and explained the effect of

long-run relationships between container throughput and

number of ship call, export, import, and GDP per capita

for one rank of vector cointegration. That means all the

factors move together to explain the changing demand for

container throughput of Cambodia’s ports in the long-run

equilibrium. The number of ship call and export has a

significantly positive impact. Alternatively, the import and

GDP per capita shows a statistically significant and

relatedly negative influence on container throughput.

Nevertheless, it is crucial to disclose the estimation

resultfrom the VECM did not find a short-run

relationship. That means, in the short-run equilibrium, the

affecting factors did not move together to demonstrate the

change of container throughput demand. It is crucial for

policy implication for government, port authorities, etc.

The best attentive insights, measures and actions would

help manage and achieve the growing container

throughput demand, yielding fruitful results in output and

performance of the port through the planning, developing

and building new ports, port facilities, infrastructures, and

terminals etc. Furthermore, the findings have also

contributed to closing the previously identified study gap.

Because the model and analysis on container throughput

has encouraged the export activities and domestic

productions and stimulated growth of economy, as well as

in order to propose and develop the maritime transport

strategic policy and development for Cambodia to support

the higher demand of ports throughput in the future.

Keywords:- Time Series, Econometrics, Vector Error-

Correction Model, Maritime Transport, Container

Throughput and Ports.

I. INTRODUCTION

According to Ng and Liu (2014), the container ports

have become the backbone of world logistics and supply chain

efficiencies, served as the crucial mode of world transport

services in terms of cost-saving, capacity benefits. Nam and

Song (2011) pointed out that sea trade through the ports

increased by nearly 90 percent in volume and roughly 70

percent in value of world trade. Scholars and industry practitioners have been drawn to study of container

throughput or performance measurement in ports and

terminals. According to Pallis et al. (2011) and Woo et al.

(2011a), the port and terminal performance could be

considered as a well-established part of the port-related

literature reviews; and Tongzon (2001); Cullinane et al.

(2004); Da Cruz et al. (2013) also demonstrated the container

throughput or port output conventionally focused on the

productivity and efficiency of port operation. With an

uncertain logistics environment, container ports have recently

faced with numerous challenges and restructuring in order to

continue and maintain. As a result, the up-to-date container ports are now parts integrated into complex systems that

operate in a volatile transportation characteristics.

This study aims to provide insights into the maritime

transport markets and ports, and to explain how important factors determine container throughput by empirically

analyzing and testing the equilibrium effects on long-run and

short-run relationships among key explanatory factors of ports

in Cambodia. Two major international ports are Phnom Penh

Autonomous Ports (PPAP), the inland port on the Tonlé Sap

River, accessed via the Mekong River, and Sihanoukville

Autonomous Port or Port Autonome du Sihanoukville (PAS)

as the seaport (PAS, 2015; PPAP, 2016). PPAP transfers

containers, vessels, barges, petroleum, and gas directly to

Phnom Penh. Sihanouk Ville Autonomous Port provides

access for other heavy goods that presently transported by road and rail. This paper also focuses on investigation and

estimation the demand for container throughput by using and

selecting the factors of determinants to see relation effects

whether there is a long-run and short-run equilibrium of port

performance to meet the supportive purposes that can give

policy implications for the port sector and maritime transport

and effective decision-making supporting tool to improve

efficiency and output.

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According to PAS and PPAP, the total container

throughput of international ports in 2000 accounted for 130,435 TEUs, increased to 222,928 TEUs in 2010 and

459,839 TEUs in 2017, respectively. In 2016 and 2017, there

were about 400,187 TEUs and 459,839 TEUs going through

PAS, and showing a 15 percent increase over 2016. PPAP’s

container throughput achieved by 184,805 TEUs in 2017, up

from 62,256 TEUs in 2010 and 746 TEUs in 2002. PPAP

came to 184,805 TEUs, up 21.76 percent compared from

151,781 TEUs in 2016. Many previous studies and researches

have also examine and mainly focused on the forecasting the

port performance and efficiency and many others conducted

the container cargo volumes and flow of the container port

throughput. However, until today there has been no any study and research to determine the factors influenced on the

container throughput of Cambodia’s International

Autonomous Ports. For that reason, this research is intending

to fill the gap by attempting to investigate and examine to

identify and measure factors that determine the container

throughput of Cambodia's International Autonomous Ports

accordingly. This research question will mainly restrict to

factors determining maritime transport markets for

Cambodia’s ports by applying the time series econometric

analysis on the container throughput of Cambodia's ports. To

analysis this framework, the econometric analysis of VECM is proposed and used STATA software program to observe the

effect of the long-run relationships between container

throughput and four key factors determined, such as the

number of shipcall, export, import, and GDP per capita. In

terms of scope, this study does not specify any other related

area of economics and management in general due to the

limited constraint of time of study and data collection. This

research study may has many fruitful and advantageous

contributions for the country and the globe based on the

maritime economics and logistics and management as a

special instrument to promote the sustainable economic

growth and development of regulative principle and strategic policy. The maritime industries, governments, and port

authorities should follow and stay updated with the fluctuating

dynamics of the maritime transport sectors, as well as

movements and growths that affect it.

II. LITERATURE REVIEW

The literature review will conduct theoretical and

empirical overviews with reference to objectives and topics. The literature review first provides the theoretical overviews

of the maritime transport market and how the maritime

transport economics are related to broad trends in academic

research of maritime transport and ports. The review then

outlines academic research on analysis of port container

throughput, characteristics of changing port business

environment, and port output measurement to establish the

guideline of the conceptual framework model about port

output indicator selection as well as identifying the

relationship between the variables. The research review will

also present and investigate previous research findings of

other researchers to determine whether the study of Cambodia's port can be defined and identified through the use

of statistical and econometrical tools to measure and

investigate the long-run relationships of container throughput,

and to set out the maritime transport policy implications.

A. Theoretical Foundation

The research’s theoretical foundation and literature

reviews are selected the best theoretical and empirical

frameworks, hypothesis development and conceptual frameworkrelated to the key variables to reveal the research

gaps of study. Furthermore, the hypothesis development is

paramount related to the research’s objectives and presented

and discussed.

a) Port Performance

Many types of measures can be used to evaluate port

performance according to Langen, Nijdam, and Horst (2007).

The most commonly used indicator is the measurement of

goods throughput volume, which can be expressed and

measured in TEU or Ton, but another measurement of

monetary value can be used as well. The investigated study of

the influence of port supply, port demand, and value-added

activity of port on the development of regional economics in

five Chinese costal port clusters performed by Deng, Lu, and

Xiao (2013) showed that supply and demand for the port had a significant positive relationship with each other. Value-added

activity was found to be significantly related to port demand,

and value-added activity had a significant influence on the

regional economy, whereas port supply and port demand had

no direct significant impact on the regional economy. Port

supply, on the other hand, has had an indirect influence on the

regional economy through port demand and value-added

activity. Similarly, port demand influenced the regional

economy indirectly through value-added activities. United

Nations proposed several port performance indicators since

the seminal works (Unctad, 1976; De Monie, 1987). In recent

years, the studies of port performance and throughput has started to rise interestingly and to be analyzed in numerous

methods with utilizing small samples size of ports all over the

world, growing continuously (Chung, 1993 &Poitras et al.,

1996).

b) Port Throughput

Unctad (1976) and MRC (2018) define the meaning of

TEU (Twenty-Foot Equivalent Unit) as the standard

dimensions of container throughput, measures 20 foot (length)

8 foot (width) 9 foot, and is used to describe the carrying

capacity of a container ship, cargo ship and container port, can

load and unload onto the ships, truck, trains and planes.

Throughput is well-defined as a number of container activities

that occur as it passes through the port terminal (Liu and Park,

2011) as well as the amount of commitment required to

transfer cargos in terms of container movements per unit of time are measured. Container throughput, according to

Langen et al. (2007), is one of the most widely used

performance measures in the port industry. Alternatively,

various factors such as the number of ship call and export and

import or transshipment cargo, berth and yard utilization,

crane and ship productivity, and service time could have an

effect on port throughput growth. All of these are internal

factors that the port company or port authority can influence

directly or indirectly. Besides the internal environmental

factors that port authorities or port operators have direct or

indirect control over must be taken into account, it is also



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important to consider external environmental factors based on

macroeconomics when evaluating the impact. When an output handle is assembled in a port, the port’s throughput is

determined by the performance of respective independent

industries (Paflioti et al., 2017).

B. Theoretical and Empirical Review Throughout many capturing and selecting literature

reviews, the study of many researchers dealt with, which just

merely or mainly compared a single port or many ports in

each country or in regional country as preferred, particularly

and practically in Hong Kong, Korea, China and ASEAN

countries etc. and many are also focus on the forecast of

container throughput. Yet there were not many articles or

researches focused on which factor has the strongest

determining or affecting to container throughput in Cambodia

and the main concern. The many attributes determined port

and the economic factors influence on attracting shipping liners and industries, and thus, throughput volume or

container throughput. The number of ship call, export, import,

and GDP per capita will be discussed as the four major

influences on the throughput of port.

a) Factor Affecting Container Throughput

The focus on this study is on the demand-side of

TEU’s output of Cambodia’s ports. From a practical

standpoint view, determining and predicting container

throughput by using the volume of throughputs in TEU rather

than the volume of cargo in Tonne produces numerous

advantages for ports, operators, and carriers (Levinson, 2006)

because operational decisions and services offered are directly

and indirectly dependent on the number of loading and

unloading changes per unit. Numerous studies examining the

factors affecting the maritime market and the factors affecting

the container port throughput has decreased significantly in recent years. To assist in analyzing container throughput

determinants, the impact on the port’s container handling

performance will need to be considered. The throughput in a

container can be measure and stored in many different

behaviors (Langen et al., 2007; Liu Bin et al., 2002; Jiang, J.

et al., 2007; ArjunMakhecha, 2015-2016). The utmost

commonly used indicator is the throughput volume of cargoes,

which is defined as the number of containers in TEU. An

increase in throughput is regarded as evidence of the output

efficiency with which container ports deal with throughput

(Langen et al., 2007). In this research, the author will present and examine the key selected determinants of container port

throughput founded by the related reviews.

b) Number of Ship Call

In literature reviews, Barros (2005); Tovar and Trujillo (2007); Gonzalez and Trujillo (2008) identified the number of

ship call as one of the multiple outputs of a port at a given

time, i.e., this study is conducted annually, as the number of

ship call. There are a number of perceptions regarding key

players in the decision making process for terminal or port

selection. Shipping lines are the most important players in

determining which port to use. Ports play an important role

when it comes to value-driven chain systems. Ports and their

services need to provide customers with consistent value

compared to their competitors in value chain systems. The

reality is that many industries, either shippers or shipping

lines, determine cargo flow, which will look for a route that

will provide the lowest price for a given level of servicewhile maintaining quality. Container ports capable of providing this

service will be selected as call ports in the logistics chain,

acting as a node in the chain (Yap and Lam, 2006). According

to Kutin (2017), after the annual throughput of containerized

goods as a general indicator, the number of ship call is another

possible indicator in measuring port’s performance. Several

researchers emphasized the number of shipcall, which refers

to the frequency with which shipping lines come and call at a

port with more competitive service and cargo flows (Lam and

Yap, 2008). In the analyses by Slack (1985); Bird (1988);

Bardi (1973); Brooks (1984 & 1985); Saleh and Lalonde

(1972), shipping lines are the key players in determining port choice, value-driven chain system, and port choice. According

to Unctad (2004), the shipping connectivity index by country

includes the number of ships, carrying capacities, and

services, as well as the number of shipping companies, ship

call, and maximum vessel size. In the calculation of PAS and

PPAP, the total number of ship call measured in the unit of the

ship also included a general cargo ship, an oil tanker, a

container ship, and a passenger ship.

The capability of a seaport to attract maximum container

ship traffics, such as ship call, container ship, and the

frequency of ship arrived at ports, are factors of container

throughput. Ship owners are increasingly selecting the port

choice to call (Huybrechts, 2002; Hilde Meersman, 2016).

Cullinane (2004); Brooks (1985) revealed that increasing the

frequency or number of ship call can attract more customers and cargoes, resulting in an increase in cargo volume and

throughput, which will have a positive and significant effect

on port performance (Song and Han, 2004). Moreover, an

increase in ship call frequencies is attracting both shippers and

shipping lines alike. Slack (1985) conducted a study that

revealed some intriguing information about port selection.

c) Import and Export

For the time being, as soon as it is aware that, not

many studies are published that review the factors affecting

container throughput volumes or port throughput handling, i.e.

the trade volume of container throughput – export and import

of container throughput. There are now only a few papers that

attempt to review a part of the literature on forecasting

container throughput within the multi-port region (Jiaweia et

al., 2012).By theoretical and empirical models and various numerical studies for the Asia-Europe and Asia-North

America trade lanes found that multi-purpose of ports was

superior in terms of the total cost. Theoretically, developing a

multi-port region in Asia trade is efficient, as evidenced by the

rapid container throughput growth. Thus, in terms of a multi-

port region, the analysis of port throughput in the North

America, Japan, and Southeast Asia and in the Pearl River

Delta is both necessary and feasible (Imai et al., 2009).

According to Drewry (2010), container throughput benefited

from the estimation of consumer price index in selected

developed and developing countries such as the United States,

the European Union, and BRIC countries such as China and India.

Therefore, these actors are required for the

operationalization of container throughput handling at ports;



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alternatively, maritime transport is the derived demand for the

economic system.Moreover, when determining the container throughput, it is important to consider the macroeconomic

components of a country's trade value, such as export and

import, among other things. Tongzon (1995) accepted that,

these factors significantly impact container throughput

handling at ports. According to some studies, the value of

import and export into a country is the most important factors

(Seabrooke, W. et al., 2003), and it is also discovered a

nation's economic development is largely responsible for port

expansion. This container throughput considers both inter-port

interactions and other influences. Nonetheless, Jiaweia et al.

(2012) confirmed that a demand-based module provides

incremental data as well as the impact of random events on the maritime industries, which was required to forecast

container throughput volumes at ports in South China's Pearl

River region during the global financial crisis.

d) GDP Per capita

Langen et al. (2012) argued that selected factors, such

as growth of GDP and growth of trade are used to prove the

existing relationship among the throughput and

factors.Langen (2003) also developed a model for analyzing

and forecasting future demand for transportation between two

countries. Within the framework of reference of the

investigation, it emphasizes the high degree of uncertainty in

trade flows and attempts to explain the pivotal need for

flexibility in economic interest decisions relating to port

investments. Andrea Mainardi (2016) explored cargo

throughput forecasting in Portuguese ports using econometric indicators related to the Portuguese economy, such as

Portuguese GDP, population, inflation, domestic

consumption, and world GDP.

C. Econometric Review According to Liu and Park (2011); Seabooke et al. (2003);

Gosasang (2011); Peng and Chu (2009); Langen et al. (2012)

conducted research on forecasting demand for container

throughput in TEU by using the regression model. The effect

of container throughput on port performance will need to be

measured to aid in analyzing container throughput

determinants. Port performance can be measured in several

ways (ArjunMakhecha, 2015-2016). Most studies that used

the regression approach to analyze container throughput used

GDP as an independent factor. Containerized trade flows can

be estimated using long-term global GDP growth, and the multiplier effect of container growth results in three to four

times GDP growth (Unctad, 2013). Liu B. et al. (2002) also

used a linear regression model to examine the relationship

between container throughput and GDP, fixed asset

investment, interest rates, and international trade. Using per

capita GDP data, Nakano H.P. (2004) discovered that rising

per capita GDP in major developed countries would lead to a

decrease in the number of containers per capita. Jiang et al.

(2007) conducted a value-based economic analysis of

container throughput, local GDP, and international trade

volume using a binary linear regression model, and Xu Xiao

(2014) also examined the relationship between natural miniaturization of GDP and container throughput using a time

series method.

The econometrics model is the most useful method for

determining the relationship between variables. If the relationship is known between them, there can be a significant

change among the variables over the forecast limit

(Armstrong, 2001). Several techniques applied to determine

and estimate or predict container volume throughput, such as

Neural Network (Gosasang et al., 2011), Regression Model

(Chou et al., 2008), Grey Forecasting Model (Qiuhong, 2009),

and Vector Error Correction Model (VECM) (Rashed et al.,

2013), etc. Much research is found in container analyzing

forecasting that indicates a correlation between

macroeconomic variables and container throughput of ports

(Chou et al., 2008b). Thus, in this study, The VECM will be

used to investigate and analyze whether a long-run relationship exists between the container throughput of

Cambodia's combined international ports, namely PAS and

PPAP. In most literature reviews, many researchers have

implemented the VECM as the best model to examine and

estimate the container volume demand in the long run effect

in different ports. The empirical reviews of the econometric

approach of VECM conducted by many researchers and

scholars to estimate the container throughput are more

accurate and reasonable than regression analysis (Syafi’I,

2005).

D. Development of Hypothesis

This study can conceptualize framework analysis on

variables in the estimated model, a basic conceptual

framework or structure organized around the theory. It defines

the kind of variables that are going to be conducted in the estimation and analysis. Four exogenous variables, namely the

number of shipcall, export, import, and GDP per capita, are

categorized as factors determining the container throughput of

Cambodia's combined international ports. Container

Throughput measures in TEU, which is the endogenous or

dependent variable. This study aims to test the four

hypotheses which are previously mentioned and considered as

the claim or assumption about the value of a population

parameter. The proposed hypotheses are as the following;

H1: The number of ship call is statistically significant with

container throughput.

H2: Import is statistically significant with container

throughput.

H3: Export is statistically significant with container

throughput.

H4: GDP per capita is statistically significant with container

throughput.

III. METHODOLOGY

A. Research Design

The correct econometric methodology and research design

are found to be critical for using and testing the data. To

begin, the unrestricted Augmented Dickey-Fuller (ADF) and Phillips Perron (PP) tests are employed to examine the

stationarity of series among variables. Second, Vector

Autoregression (VAR) is utilized to select the most

appropriate lags. Third, the Johansen Cointegration test is

performed to see if there are any cointegrating vectors or

maximum ranks. The cointegration test determines long-term

cointegration amongst variables. Fourth, constraints are



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placed in the model of VAR, and VECM is estimated to

determine effects of long-run effects and short-run dynamics. To maintain the current results and findings, Impulse

Response Function (IRF) is also used in this study.

Furthermore, model diagnostic tests such as system stability

tests, autocorrelation, heteroskedasticity, and normality tests

are run to ensure the model’s reliability and validity and to

verify the findings. The VECM, on the other hand, is

estimated using the STATA/IC15.0 software program.

Unknown parameters can be determined using a variety of

analysis techniques and estimates. Nonetheless, the VECM

model is much more efficient and accurate in its research and

detailing of long and short-run equilibrium relationships.

B. Data for Analysis

This research is utilized the quantitative approach from

1992 to 2017 to analyze and assessed in annual time series

data. Further, the dataset was transformed to logarithm to get elasticity coefficients. The data of container throughput of

Cambodia’s port was included import and export of laden and

empty container throughput in TEUs. Container throughput

data was collected from PAS and PPAP and aggregated and

combined into the total container throughputs of Cambodia’s

ports. In addition, container throughput variable is used as

dependent variable. The number ship call measured in unit of

ship was also collected from PAS and PPAP ports of

Cambodia, and included general cargo, oil tanker, container

and passenger ship. Export and import and GDP per capita

value measured in USD million which was collected from the

IMF. The data of annual GDP per capita was calculated by dividing annual GDP to total population of Cambodia from

1992 to 2017.

C. Econometric Model

The container throughput was taken as the dependent variable. The number of ship call, export, import, and GDP

per capita were chosen as the independent factors to explain

and control container throughput in Cambodian ports.

Multiple regression analysis was used to identify factors that

are related to the dependent variable and to investigate the

nature of the relationship as well as to conduct research,

prediction, and forecasting. Multiple regression models were

used to investigate causal relationships between independent

and dependent variables.

The equation formula and selected variables are written

as follows:

lncontt = β0+β1lnnsct+ β2expt+ β3impt + β4gdppct+ εt (1)

Where,

Container Throughput in TEU = cont

Number of Ship Call in Unit = nsc

Export in USD = exp

Import in USD = imp

GDP Per Capita in USD = gdppc

β0: The Intercept β1, β2, β3, and β4: Coefficients

t: Time Series

εt: Error-Term

All variables are basically transformed into natural

logarithms. Transforming the nature of dependent and

independent variables to logarithmic form help us to measure

the elasticity of dependent variable with respect to the independent variables. β0 is constant or intercept, and β1, β2,

β3, and β4 are the parameters or slopes measured by each

independent variable, and whereas the εt is the long run error-

term or random disturbance term. The time series econometric

model was used in this study to investigate cointegration

vectors and causality directions of demand for container

throughput of combined ports in Cambodia.

D. Step of Estimation

Step 1: Data must be stationary.

Step 2: Optimal lag is selected.

Step 3: Johansen cointegration test is identified.

Step 4: VECM is estimated.

Step 5: Diagnostic tests is performed.

E. Time Series and Stationary

The time series model uses the movements of variables in

the past to estimate values and results in the future. The

timeseries model, according to Stock and Watson (2003), is

not economic theory. However, unlike structural models, the

predictability of the estimated equation should be evaluated based on the model's performance outside the sample.The

timeseries econometric model, according to Keck, Raubold,

and Truppia (2009) can produce relatively accurate

forecasting in most cases, particularly when variables have

multi-dimensional relationships. Because of its complexity, it

is vulnerable to omitted variable bias, misspecification,

multiple simultaneous causalities, and other issues, all of

which can result in significant forecasting errors.According

Gujarati (2012) pointed out that the value of covariance

between two time series of period is determined only by the

distance or gap between two time series of periods, rather than the actual time series at which the covariance is computed,

indicating that a time series is stationary if its mean and

variance remain constant over series of time.

F. Unit Roots Test Unit root tests are used to determine whether or not data

sets are stationary. According to Gujarati (2008), the unit root

test is a stationarity or nonstationarity test that has received

widespread popularity over several years due to simplicity

(Gujarati, 2008). To evaluate if the series is stationary in level

I(0) or nonstationary in level, but stationary in first

differencing I(1), i.e. integrated of degree on I(I), the unit root

test of time series is carry out . Dickey-Fuller suggested that

ADF for unit root test (Engle and Granger, 1991) is conducted

with a constant and trend term based on the critical values.

The null hypothesis test of unit root is the only assumption that is rejected. To determine the appropriate or optimal

lagged selection to ensure that the error becomes

approximately white noise, the Akaike Information Criterion

(AIC) and the Schwarz Information Criterion (SIC) are used

for testing purposes. Standard tools for stationary testing

include the enhanced Dickey-Fuller and Phillip-Perron

methods. Because the PP test corrects for serial correlation,

autocorrelation and heteroskedasticity problem in the errors

disturbance, the estimation finds preferable to the ADF test

for many applications. Furthermore, PP tests do not

necessitate lag selection and are based on a serially linked



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regression error term rather than a random effect model. ADF

tests are similar in that estimates are centered on the null hypothesis that the series is not stationary. The null for PP

tests are similar (Dickey F., 1979; Perron P., 1989).

G. Cointegration Test

Engle and Granger (1987) proposed that presence of number of cointegration vectors addressed the problem of

integrating the dynamic effect of short-run relationship with

long-run equilibrium. This approach, which has been

frequently utilised in quantitative studies but has a number of

drawbacks that include distortion in sample size and non-

unique characteristics depending on the normalization

variable employed, cannot identify numerous cointegrating

vectors (Banerjee et al., 1993). Engle and Yoo (1991);

Johansen (1991, 1998) proposed and suggested a three-step

approach and maximum likelihood model. There are

numerous cointegration tests, and cointegration may have when the test is performed. The most commonly used is the

multivariate test based on the autoregressive representation

developed by Johansen (1988 & 1991); Johansen, Juselius

(1990 & 1994); Gonzalo (1990 & 1994). Johansen and

Juselius (1988 & 1991) were the first to identify the

cointegrating vectors in a system, providing a maximum

likelihood technique to test for the existing presence of

cointegrating vectors in the system. This method, which is a

variation of the VAR method, is commonly referred to as

Johansen Maximum Likelihood or VECM in the scientific

literature reviews. In the VAR framework, all of the variables

in the VAR model are identified and evaluated utilizing theoretical economic theory. It needs to conduct a pretest of

the series for unit root before applying the VECM technique.

To identify the number of cointegration of Johansen process,

the Johansen maximum likelihood technique provides and

constructs two different types of likelihood ratio tests: the

trace test and the max eigenvalue test, which are both

employed. The Johansen technique has better properties than

other estimators (Johansen &Juselius, 1990, 1994; Gonzalo,

1994), and the finite sample properties are consistent with

asymptotic findings. The VECM can be analyzed by using the

Johansen technique, which includes the error correction term or speed of adjustment in each equation. When the computed

trace test and max eigenvalue test statistics are greater than

the critical values of 1 percent, 5 percent, or 10 percent, the

null hypothesis of no cointegration is not accepted. If the

estimates of trace and eigenvalue statistics are less than the

critical value, the alternative hypothesis of one or more

cointegrating vectors is accepted.

H. Vector Error-Correction Model (VECM)

The VAR equation is one of the particular variants of the

system simultaneous equation. The VAR model can be

employed if all of the variables are stationary. When the

variables in the vectors, on the other hand, are not stationary,

then the model used is the VECM if the variables in the

vectors have at least one or more cointegration relationships.

Enders (20150) defined VECM as a VAR that built

specifically for use with nonstationary data that has a cointegrating relationship with the original data. The time

series model is one of the few that can accurately analyze the

level at which a variable can be restored to equilibrium

following a shock to other variables. The VECM method is

extremely beneficial for estimating the short-run and long-run effects in series data. Under the assumption of cointegration,

the VAR can be represented as VECM, as shown below.

∆contt = α+ ψcontt−1 + ∑ Гi∆𝑐𝑜𝑛𝑡t−i + θZt−1k−1i=1 + εt (2)

(2)

α = The Intercept

ψ = Vector of Intercept (αβ’)

k-1 = Lag Length

Г = Vector of Variables [cont, nsc, exp, imp, gdppc]

θ = Speed of Adjustment

εt = Residual or Error-Term Zt-1 = Error-Correction Term (ECT)(-1)

contt = Vector of Lagged Variable

β’contt, Δcontt = Coefficient of Vectors

Utilizing an econometric model is a clear framework for

developing and testing economic hypotheses. The model

demonstrates anfull insights of the effect and dynamic

behavior. The use of likelihood methods providesfor

undertaking inference without the need to derive estimator

and tests.

Unrestricted VAR is the estimate of ψ matrix applied and

tested by using the Johansen's approach whether the restricted

suggested by the reduced rank of the matrix is rejected. This

equation can be used to indicate the rank N x N matrix as ψ =

α x β’ where N x R matrix of rank. According to the model

employed in this investigation, N = 5, cont = (cont, nsc, exp,

imp and gdppc). b’ contt is stationary and is viewed as long-

run equilibrium relationship between the jointly influenced

factors because b’ is a matrix that indicating the cointegration

relations, It is critical to highlight that unless a normalization or identification procedure is specified, it is impossible to

predict the individual coefficient b’. In the short run,

stochastic shocks may push and drive the system, but in the

presence of a cointegration relationship, pushing variables that

will lead the system to intersect with the long-run

relationship.The stationary process proved byb’contt, and the

speed of adjustment coefficient formed by A for the equation

showed the equilibrium relation and deviation. The max

eigenvalue and the trace test are utilized to determine the

number of cointegration vectors under the ψmatrix of reduced

rank hypothesis. Ifψ has a rank of zero, no stationary linear integration can be found, indicating that the variables in contt

are not cointegrated. If rank R of ψ matrix > 0, then R is not

possible nonstationary linear integrations after considering the

parametric constraints implied by long-run relationships, the

short-run function is estimated steadily in consistent manner.

In response to short-run disequilibrium, the VECM helps

numerous factors that influence simultaneous dynamic

adjustments at different rates than the others because theory is

frequently unsatisfactory for defining the dynamic adjustment

process. By presenting a multivariate VECM, this study aims

to investigate how the key elements of independent variables affect container throughput.

I. Long-Run Causality

Johansen and Juselius (1990) developed the economic

relationship of long-run equilibrium effect. The Johansen



ISSN No:-2456-2165


cointegration test typically comprised of 3 general steps,

which are as follows: First, ensure that all variables in the model are integrated in the same order by running unit root

tests on each variable. Second, calculate the appropriate lag

selection for the VAR model in order to ensure that the

estimated residuals are not autocorrelatedwith the observed

residuals. Three, Enders (2004 & 2010) proposed to estimate

VAR model in order to generate the cointegration vectors,

which identify the rank of cointegration using the trace test

and the max-eigenvalue statistical tests. The coefficient of

error-correction term (ETC) of one-period lagged value

indicates the causality of long-run effect and expresses the

adjustment speed. The expected value of 𝛽t should be

significant and have a negative sign. 𝛽t (-1&0) of absolute

value indicates how quickly the equilibrium effect is

reinstated and restored. The significance of ECT implies that

the long-run equilibrium relationship of the independent

variable is moving and can explain the effect on dependent

variable.

J. Short-Run Causality

The jointly significant variables describe how the

endogenous variables respond to short-run dynamics. This

study, as discussed by Hakim and Merkert (2016), also

assesses the jointly significant level of the coefficients of the

endogenous variables 𝛾t and 𝛿t by testing the short-run

relationships effect.

K. Impulse Response Function

It is intriguing to determine the shocking reaction by

performing the Impulse Response Function (IRF) in one

variable to an impulse in another variable in a system with several other variables. It is said to have the latter occurrence

of the causally responsible for the earlier occurrence if there is

a shocking reaction in one factor of variable to an impulse in

another factor. The IRF takes a look for the moving average

reaction and explains how the factor influences a shocking

reaction over time to a single response growth in itself or any

other variables influences a variable over time to a single

response growth in itself. According to Lutkepohl’s (1991) research, the impulse shock or effect of dynamic multipliers

can be acquired using a K-dimensional VAR model with an

infinite moving average factor. The impulse shocking is an

effective and efficient measure for determining and a useful

tool in assessing the stability of the estimated equation within

the framework of the vector autoregression model. The

combined effect of shocking reactions and response to zero

indicates that a system is stability.

L. Post Diagnostic Check

The statistical diagnostic tests such astest for stability,test

for multivariate heteroskedasticity, and LM test of

autocorrelation and testing for normality was performed and

carried out on the VECM model to verify that it represents the

data generating process adequately and the appropriated

model.

IV. RESULT AND DISCUSSION

A. Descriptive Statistics

The econometric analysis could be guided by the nature of

available time series data and patterns of relationship between

container throughput and number of ship call, export, import,

and GDP per capita. The findings of descriptive analyses may

also provide better hints and insights about the causal

relationships of the variables discussed in the following sections. The growth of container throughput at Cambodia's

combined ports has been studied in relation to the number of

ship call, export, import, and GDP per capita. For comparison

purposes, the ports that have encouraged and enabled

Cambodia's maritime transport market are also taken into

account. Correlation coefficients could present the

relationship between two variables.

Summary Statistics of the Variables (1992 to 2017)

Variable Obs Mean Std. Dev Min Max Skewness Kurtosis

lncont 26 11.98846 1.02611 9.96 13.38 -0.66849 2.2884

lnnsc 26 7.578077 0.365885 6.84 8.12 -0.45156 2.4558

lnexp 26 8.297308 1.066469 6.55 9.97 0.08363 1.630

lnimp 26 7.781538 1.377768 5.22 9.7 -0.36930 1.9906

lngdppc 26 6.231538 0.5829147 5.49 7.23 0.3128 1.5741

Table 1: Descriptive Results

Source: Author’s calculation

It has been noted that lncont has the highest average of

11.99 percent, while lngdppc has the lowest average of 6.23

percent. According to table 1, the lnimp is the most volatile at

1.378 percent, ranging from 5.22 percent to 9.7 percent,

whereas the lnnsc is the least volatile at 0.36 percent. Table 1

also demonstrates that lncont, lnimp, and lnsc are negatively

skewed, whereas lnexp and lngdppc are positively

skewed. A normal distribution curve's kurtosis measures the

peak in the center of the curve. The distribution is described

as mesokurtic, which implies that it is normal, if the value is close to three. The distribution is leptokurtic as long as the

number is greater than or equal to 3. As a result, all of the

series with values less than 3 in table 1 exhibit platykurtic

behavior (Wooldridge, 2010).



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Correlation Coefficients Between Variables

Variable lncont lnnsc lnexp lnimp lngdppc

lncont 1

lnnsc 0.9684 1 lnexp 0.9326 0.9546 1

lnimp 0.9834 0.9716 0.965 1

lngdppc 0.8805 0.9185 0.9857 0.9236 1

Table 2: Correlation Coefficients

Correlation Coefficients Between Variables

Variable lncont_d1 lnnsc_d1 lnexp_d1 lnimp_d1 lngdppc_d1

lncont_d1 1

lnnsc_d1 0.3871 1

lnexp_d1 0.3497 0.3228 1

lnimp_d1 0.2108 0.1127 0.0972 1

lngdppc_d1 0.1832 0.3132 0.4968 -0.1564 1

Table 3: Correlation Coefficients


Before establishing the VECM and avoiding a multi

collinearity problem, the correlation analysis of all factor

variables will be performed. If there is a correlation between

the independent variables and they have an approach to

influence the dependent variable. The correlation matrix of

two-sided Pearson Correlations between all variables is shown

in tables 3 and 4. Correlations show that all variables are related to one another. Number of ship call, export, import,

and GDP per capita all has significance and strong

relationships with container throughput. Table 2 shows the

coefficient correlation for the entire series. The results show

that the model's dependent variables have a strong positive

correlation relationship. Because each dependent variable is

highly correlated, their presence in the same model will result

in a multicollinearity problem. As a result, it is decided to

look into their impact on the first difference. Similarly, the

results of the variables at first difference presented in Table 3

show a positive relationship, with the exception of the correlation coefficients between lnimp_d1 and lngdppc_d1.

The covariance matrix in tables 4 and 5 shows that the

coefficients of all variables are positive, indicating that as one

variable increases, so does another. But except for the

negatively related coefficient between lngdppc_d1 and

lnimp_d1. Descriptive analysis and graphical representative

indicate that there is most likely a long-run and short-run

relationship between the study's dependent vs. independent

variables.

Covariance Matrix between Variables

Variable lncont lnnsc lnexp lnimp lngdppc

lncont 1.0529 lnnsc 0.363581 0.133872

lnexp 1.02054 0.372487 1.13736

lnimp 1.39023 0.489775 1.41786 1.89825

lngdppc 0.526687 0.195895 0.612784 0.741801 0.33979

Table 4: Covariance Matrix

Covariance Matrix between Variables

Variable lncont_d1 lnnsc_d1 lnexp_d1 lnimp_d1 lngdppc_d1

lncont_d1 0.024689

lnnsc_d1 0.004982 0.006708

lnexp_d1 0.007393 0.003557 0.018098

lnimp_d1 0.00558 0.001555 0.002201 0.028374

lngdppc_d1 0.002082 0.001856 0.004835 -0.00191 0.005233

Table 5: Covariance Matrix




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B. Stationarity Check

Before selecting an appropriate model, performing estimation, and avoiding erroneous analysis and

spurious results, it is necessary to determine whether each

variable is stationary or nonstationary. The purpose of this

analysis is to see if the data has stationarity properties using

graphical analysis, Dickey-Fuller, and Phillips-Perron tests.

Table 10 shows the results of two different tests that were

carried out. Both test revealed that the series levels

under discussion are I(1), that is, not stationary. Furthermore,

the graph of autocorrelation and the partial autocorrelation

function (ACF) are shown. Correlogram techniques are also

used to ensure the accuracy of the results.

The VECM model technique is being used to determine

whether there is a relationship between container throughput

and the number of ship call, export, import, and GDP per

capita. It is also interesting in determining a long-run effect and equilibrium. Before estimating the model, the statistical

conditions of series must be checked to determine whether

they are stationary or not. The graphs of the series under

consideration are depicted in Figures 1 and 2. If the time series is not stationary, the series may be typically converted

to stationarity using a technique known as differencing.

Because the variables in Figure 1 are non-stationary, so it is

best thing to turn them into stationary variables. To be valid,

the VECM model requires that the time series must

be stationary. The stationary variables after first differencing

are shown in Figure 2. The graph depicts a constant mean and

variance over time. It is necessary to conduct Q-statistic tests

to determine whether the variables are correlated to assess the

absence of autocorrelation. Correlograms of all variables at

the same significance level show that the relationship is

statistically and significantly related. The Q-statistic test is used to determine whether or not there is an autocorrelation

issue. Table 6 displays the Correlogram Q-statistics test of

lncont.

Fig. 1: Trends of All Variables Fig. 2: Trend of All Variables at I (1)

68

10

12

14

1990 2000 2010 2020year

lncont lnnsc

lnexp lnimp

lngdppc

-.2

0.2

.4.6

1990 2000 2010 2020year

lncont_d1 lnnsc_d1

lnexp_d1 lnimp_d1

lngdppc_d1



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Table 6: Results of Correlogram lncont

Table 7: Results of Correlogram lncont_d1


Autocorrelation Coefficient (AC) is close to zero,

indicating that the autocorrelation problem has been fixed.

The Q-statistic test result specifies that, despite being

statistically significant, Partial Autocorrelation Coefficients

(PAC) for all other lags is not statistically significant. Table 7

shows that the correlation Q-statistic test lncont_d1 is not statistically significant at the 0.05 of critical level, indicating

that there is no significant serial correlation in the model. As a

result, the test estimates imply that there is no evidence of

autocorrelation. The results show that the observed Chi-square

probability value is not statistically significant for critical

values of 5 percent or less. It is concluded that the estimated

model contains no presence of serial correlation. In this regard,

the model is appropriate for economic analysis. The graph

demonstrates that AC and PAC are significant at the level.

Because the p-values of the Q-statistic test are greater than 5 percent after converting the time series data to I(1), the null

hypothesis for all variables can be accepted. As a result, each

variable confirms to be stationary. Figure 4 shows that there is

no evidence of autocorrelation in the white noise.

11 -0.1094 0.2616 65.779 0.0000

10 -0.0480 0.1649 65.198 0.0000

9 0.0186 0.1902 65.093 0.0000

8 0.0762 0.1008 65.079 0.0000

7 0.1601 0.2326 64.844 0.0000

6 0.2574 0.0419 63.862 0.0000

5 0.3617 -0.2132 61.451 0.0000

4 0.4652 -0.0296 56.915 0.0000

3 0.5849 0.3124 49.754 0.0000

2 0.7402 -0.0450 38.926 0.0000

1 0.8753 0.9521 22.309 0.0000

LAG AC PAC Q Prob>Q [Autocorrelation] [Partial Autocor]

-1 0 1 -1 0 1

10 -0.1553 -0.1287 7.708 0.6573

9 -0.1484 -0.0929 6.6228 0.6763

8 -0.0182 -0.0989 5.6934 0.6815

7 -0.0028 0.0563 5.6802 0.5775

6 -0.1719 -0.1503 5.6799 0.4600

5 0.0722 0.0122 4.6304 0.4626

4 0.2808 0.2711 4.4543 0.3480

3 0.0382 0.1453 1.9197 0.5892

2 -0.2270 -0.2437 1.8748 0.3916

1 0.1135 0.1140 .36254 0.5471

LAG AC PAC Q Prob>Q [Autocorrelation] [Partial Autocor]

-1 0 1 -1 0 1



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Fig. 3 : Autocorrelation at I(1) Fig. 4 : Autocorrelation at Level


C. Unit Root Test

The logarithmic form of the original series were utilized to

solve and eliminate the possible problem of heteroskedasticity and to make the series more comparable between themselves.

The Phillips-Perron test of stationary similarly reveals that all

level series have unit roots; however, first differencing series

are determined to be stationary at least at the 5 percent

significant level. To ensure robust results, the Dickey-Fuller

and Phillips-Perron tests are carried out by applying a constant

and trend term to the test. Because the null hypothesis of unit

root is not accepted at I(1), and the variables become

stationary at I(1), which is also known as integrated order 1 or

I(1) (1). The Dickey-Fuller and Phillips-Perron tests are

analyzed to validate the confirmation of results. The estimated

outcome demonstrated that all variables uncovered are nonstationary at the level. Because the null hypothesis is not

accepted, it turned out that these variables become stationary

at I (1). Tables 10 shows that the augmented Dickey-Fuller

and Phillips-Perron models used to evaluate the data are

stationary. The Dickey-Fuller and Phillips-Perron tests have a

critical value of -4.380 at a 1 percent significance level, -3.600

at a 5 percent significance level, and -3.240 at a 10 percent

significance level. For the 5 percent, the nonstationary null

hypothesis is rejected.

Unit Root Test

Variables

Augment Dickey Fuller Phillips-Perron

Order Test Statistic Test Statistic

Level First Difference Level First Difference

lncont -1.379 -4.582 -1.309 -4.648 I(1)

lnnsc -2.801 -5.299 -2.680 -5.375 I(1)

lnexp -2.303 -5.571 -2.416 -5.566 I(1)

lnimp -1.907 -6.933 -1.872 -6.699 I(1)

lngdppc -1.773 -3.765 -1.853 -3.810 I(1)

Table 10: Dickey-Fuller and Phillips-Perron Tests


D. Lag Length Selection

The long-run relationship effect is estimated using the Johansen cointegrating technique, which tests for the

significant existent presence of the long-run relationship

effect.However, before running this test, which employs a

VAR model, it is necessary to ensure that the VAR's lag

specification is correct. The estimated result of this test is

positive, as shown in table 11. The analysis results show that

all variables are nonstationary at level but stationary in I(1)

after differencing. However, before performing this test,

which makes use of a VAR model, it is necessary to ensure

that the VAR's lag specification is valid. As stated in table 11,

the estimated result of this test is positive. The analysis result shows that all variables are nonstationary but stationary in I(1)

after differencing at level. It is possible to determine whether

or not these series are cointegrated in the long run equilibrium

by consistently integrating them across time. To estimate the

long-run effect between the variables, the appropriate

lagged order of the VAR model must be determined using

lagged selection criteria. Because of the series' small sample

size, the 2 lag selection will be applied by auto lag selection.

-0.5

00

.00

0.5

0A

uto

co

rre

latio

ns o

f ln

co

nt_

d1

0 2 4 6 8 10Lag

Bartlett's formula for MA(q) 95% confidence bands

-1.0

0-0

.50

0.0

00

.50

1.0

0A

uto

co

rre

latio

ns o

f ln

co

nt

0 5 10Lag

Bartlett's formula for MA(q) 95% confidence bands



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Table 11: Appropriate lag selection


E. Cointegration Test

The Johansen technique is used to determine the existence

and number of cointegrating vector relationships, which is

used to calculate the tests of max eigenvalue (max) and trace

statistics. The null hypothesis of absent cointegration, that is,

no cointegration (Rank = 0), is not accepted by the max and

trace test statistic at a 1 percent or 5 percent significance level. As a result, the null hypothesis is not counted by the max and

trace test results. On the contrary, it is not rejected the null

hypothesis in these criteria where r ≤ 1,2,3,4 compared to the

alternative hypothesis with the value of r = 2, 3, 4 at 1 percent

significance level. Table 12 shows that the λmax test result of

44.6275 is not less than the critical value of 33.46 and 38.77 at

0.05 and 1 percent significance level, thus revealing the null

hypothesis of no cointegration is not accepted, and there is at

least one cointegrating vectors of equation. Table 12 further

supports this conclusion. The calculated trace test of 96.1011

is also greater than the critical values of 68.52 and 76.07 at

level 1 percent or 5 percent that is the evidence of

cointegration in the series. The λmax test result of 51.4737 is

still more than the critical value of 47.21. However, less than

the critical value of 54.46 at 1 percent significance level.

However, the results show that the λmax 25.4173 is not

greater than the critical values of 27.07 and 32.24 at 1 percent or 5 percent, respectively. Thus, the null hypothesis is not

rejected, and one cointegrating equation cannot be rejected.

The value of optimal lag length and the optimal order of

vector autoregressive model are chosen 2 shown in Table 11.

By Johansen's approach result, it is found that there are one

cointegration equations shown at both 5 percent and 1 percent

level at the Max-Eigen value or Max statistic value result, and

also one cointegration equations at the same level of 1 percent

in the trace results.

Johansen Tests for Cointegration

Rank Parms LL Eigen Value Trace Test Max Test 1% Critical Value 5% Critical Value

0 30 125.72347 . 96.1011 44.6275 76.07 68.52

1 39 148.0372 0.84425 51.4737*1 25.4173*1 54.46 47.21

2 46 160.74584 0.65322 26.0564**5 14.3436 35.65 29.68

3 51 167.91766 0.44990 11.7128 9.6180 20.04 15.41

4 54 172.72665 0.33018 2.0948 2.0948 6.65 3.76

5 55 173.77404 0.08358 - 44.6275 - -

Table 12: Trace and Max Statistic Test


As a result, this study concludes this study opines that

one identified cointegration relationships are measured in the

Johansen approach among the variables at the 1 percent

significance level. This connection among variables specifies

the significant identification of a long-run economic

relationship. This link among the variables indicates the

significant identification of a long-run relationship. Finally,

only one cointegration vector equation's null hypothesis can be accepted. As a result, the cointegration test indicates that

the VECM, rather than a VAR, is appropriate for analyzing

the relationship among factors such as container throughput,

number of ship call, export, import, and GDP per capita

because the series are well-cointegrated.

F. Vector Error-Correction Model (VECM)

Therefore, this paper has applied this model to estimate the

long-term container throughput as combined international

ports of PAS and PPAP in Cambodia. The cointegration result

by the Johansen approach reveals that the influenced factors

of variables are cointegrated and have a long-run equilibrium

relationship. The calibration of the VECM is carried out using

the Johansen procedure for the estimation period from 1992 to 2017 with the time series data. VECM estimation result is

presented in Table 14, 15 and 16 as below. The number of

cointegrating equation among container throughput, number

of ship call, export, import, and GDP per capita in the

analyzed model is determined, and the estimation of VECM

parameters of multivariate time series is carried out, and the

output of the estimation is provided and seen that equation is

statistically significant and the model fits well.

Exogenous: _cons

Endogenous: lncont lnnsc lnexp lnimp lngdppc

2 173.774 75.96* 25 0.000 5.0e-11* -9.89784* -9.1816* -7.19813

1 135.794 208.31 25 0.000 1.1e-10 -8.81619 -8.42552 -7.34362*

0 31.6382 7.5e-08 -2.21985 -2.15473 -1.97442

lag LL LR df p FPE AIC HQIC SBIC

Sample: 1994 - 2017 Number of obs = 24

Selection-order criteria

. varsoc lncont lnnsc lnexp lnimp lngdppc, maxlag(2)



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In Table 14, the results of the VECM model with all

variables such as container throughput, number of ship call, export, import, and GDP per capita are found to be

statistically and strongly significant at the 1 percent level of

significance, indicating a strong effect among all variables in

the long run equilibrium relationships. As a result of the

cointegration test establishing that the data is cointegrated, it

is now appropriate to estimate the VECM. Table 15 displays

the outcome of one cointegration vectorfor Cambodia's ports

in identifying the long-run relationships among variables.

The long-run equilibrium relations in the VECM are

represented in table 16 by the coefficients of the one rank of

cointegrating equation and trend and constant term. In the

long run, coefficients on lnexp and lnnsc have strong positive

effects on lncont, whereas coefficients on lnimp and lngdppc

have strong negative effects on lncont. The estimates of the

one cointegrating coefficient in the long-run equilibrium relationship (Johansen normalized on the coefficient) can be

interpreted as indicating that all coefficient parameters are

statistically significant at the 1 percent and 5 percent level.

The estimated coefficient of error-correction term (ECT) or speed of adjustment is negative sign of the coefficient on

_ce1 L1 value - 0.312 (error term from the first cointegrating

and lagged one-period) and strong significant at the 1 percent

and 5 percent level, which is the correct sign (negative) for

equilibrium and the probability of p-value is significant at

the 1 percent and 5 percent level. It is confirmed that there

is long-run economic equilibrium between dependent and

independent variables exist over time. The long-run

equilibrium causality is moving and running from lnnsc,

lnexp, lnimp, and lngdppc to lncont towards the equilibrium.

This implied that the ECM is well specified, well-identified.

This confirmed the expectation of a dynamic

adjustment toward long-run economic equilibrium relations.

Thus, if the error term _ce1 is negative in period t-1, which

can be seen as the demand for container throughput of PAS and PPAP being too high in comparison to the equilibrium

relationship other four determinants, then d cont must rise,

which can happen if the coefficient for _ce1 is negative and

significant. The greater negative coefficient on _ce1 L1, the

faster the correction.

To begin with, lnnsc and lnexp have a significant

positive effect on lncont. In other words, it suggests that, in the long run, an increase in demand for Cambodia's port's

container throughput is associated with an increase in the

number of shipcall that travelled (arrival and departure from

ports) and an increase in the export of cargo that port handled.

Second, lncont is significantly negatively affected by lnimp

and lngdppc. In the long run, it proves that the increase in

container throughput at Cambodia's ports is explained by the

deduction of imports and GDP per capita, and vice versa. This

can be expressed as an equation as follows;

lncont = –16.34352 + 1.82213lnnsc + 1.00068lnexp –

1.51536lnimp – 1.00532lngdppc (3)

The estimate result can be interpreted as long-run

elasticities. A 1 percent increase in the number of shipcall is

expected to increase container throughput by 1.82 percent,

and this estimate is significant at the 1 percent level. When

export rises by 1 percent, container throughput rises by 1

percent in response. Import is found to be negative to

container throughput in the long run equilibrium relationship.

A 1percent decrease in imports leads to a 1.5 percent increase in container throughput; this coefficient is significant at the 1

percent significance level. Similarly, a 1 percent decrease in

GDP per capita will have an increased impact on container

throughput of nearly 1 percent as well.

Following that, the estimates of the adjustment

parameters in table 17 for one cointegration equation, lncont,

lnnsc, lnexp, and GDP per capita are significant at the 5

percent level, except for lnimp. From the perspective of

empirical economic interpretation, the question is whether

imports adjust when the cointegration equation is out of long-

run equilibrium or disequilibrium.

α̂ (-0.3125, -0.2082, -0.2329, 0.1574,-01142)

β̂ (1.0000, 1.8221, 1.0006, -1.5153, -1.0053)

V̂ (0.0565, 0.0139, 0.1331, 0.3689, 0.0567)

Г̂

(

0.3805 0.7291 − 0.3496 − 0.2390 − 0.11520.2148 0.0074 0.0539 − 0.2558 − 0.1334−0.0963 − 0.1429 − 0.1416 − 0.2341 0.2146−0.1010 0.0765 0.0455 − 0.297 − 1.4128−0.0696 − 0.0541 0.1821 − 0.1904 0.0927 )

Table 14: Vector Error-Correction Model

D_lngdppc 7 .056013 0.7774 59.36823 0.0000

D_lnimp 7 .128344 0.7872 62.87356 0.0000

D_lnexp 7 .118232 0.7186 43.41968 0.0000

D_lnnsc 7 .073772 0.5897 24.43424 0.0010

D_lncont 7 .151375 0.6201 27.75378 0.0002

Equation Parms RMSE R-sq chi2 P>chi2

Det(Sigma_ml) = 3.02e-12 SBIC = -7.172096

Log likelihood = 148.0372 HQIC = -8.578559

AIC = -9.086434

Sample: 1994 - 2017 Number of obs = 24

Vector error-correction model



ISSN No:-2456-2165


Table 15: Johansen Normalization Restriction Imposed


Table 16: VECM

.

_cons -16.34352 . . . . .

lngdppc -1.005326 .5167297 -1.95 0.052 -2.018098 .0074452

lnimp -1.515368 .15391 -9.85 0.000 -1.817026 -1.21371

lnexp 1.000689 .402085 2.49 0.013 .2126167 1.788761

lnnsc 1.822134 .4472684 4.07 0.000 .9455042 2.698764

lncont 1 . . . . .

_ce1

beta Coef. Std. Err. z P>|z| [95% Conf. Interval]

Johansen normalization restriction imposed

Identification: beta is exactly identified

_ce1 4 835.2753 0.0000

Equation Parms chi2 P>chi2

Cointegrating equations

D_lnnsc

_cons .0565418 .066212 0.85 0.393 -.0732312 .1863149

LD. -.115222 .5276947 -0.22 0.827 -1.149485 .9190407

lngdppc

LD. -.2390774 .2299161 -1.04 0.298 -.6897048 .2115499

lnimp

LD. -.3496832 .2947484 -1.19 0.235 -.9273795 .228013

lnexp

LD. .7291129 .4489831 1.62 0.104 -.1508778 1.609104

lnnsc

LD. .3805853 .2570005 1.48 0.139 -.1231265 .8842971

lncont

L1. -.3125449 .1304574 -2.40 0.017 -.5682367 -.0568531

_ce1

D_lncont

Coef. Std. Err. z P>|z| [95% Conf. Interval]



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Table 17: Adjusted Long-Run Relationships


Table 18 : Short-Run Causality


Overall, the results show that the equation model fits

well. The coefficients on lncont, lnnsc, lnexp, and lngdppc in

the cointegrating equation are statistically significant. The

ECT, or speed of adjustment toward equilibrium of lncont, has

a negative sign and is statistically significant (ce1 L1. = -0.31

and p-value = 0.017), confirming the existence of adjustment

toward equilibrium and identifying the existence of a long-run causality running from lnnsc, lnexp, lnimp, and lngdppc to

lncont.The error-correction components in the VECM

represent the speed of adjustment to the equilibrium.

Typically, the ECT should be between 0 and 1 and must have

a negative sign in value (Brooks, 2008). Table 18 shows the

estimates, standard errors, z statistics, and confidence

intervals for the short-run parameters. The parameters in this

model's adjustment matrix are the five coefficients on L. ce1.

The L. ce1 of D_lncont, D_lnnsc, D_lnexp, and

D_lngdppc is significant, indicating that the adjustment to

equilibrium is relatively slow. Table 18 shows the statistical

procedure used to test for short-run causality between these

variables. The null hypothesis is zero, which means it cannot

L1. -.114285 .0482728 -2.37 0.018 -.208898 -.019672

_ce1

D_lngdppc

L1. .157439 .1106095 1.42 0.155 -.0593517 .3742297

_ce1

D_lnimp

L1. -.2329378 .1018948 -2.29 0.022 -.432648 -.0332276

_ce1

D_lnexp

L1. -.2082494 .0635784 -3.28 0.001 -.3328608 -.0836381

_ce1

D_lnnsc

L1. -.3125449 .1304574 -2.40 0.017 -.5682367 -.0568531

_ce1

D_lncont

alpha Coef. Std. Err. z P>|z| [95% Conf. Interval]

D_lngdppc 1 5.604963 0.0179

D_lnimp 1 2.026001 0.1546

D_lnexp 1 5.226074 0.0223

D_lnnsc 1 10.72874 0.0011

D_lncont 1 5.739677 0.0166

Equation Parms chi2 P>chi2

Adjustment parameters

Prob > chi2 = 0.3397

chi2( 4) = 4.52

( 4) [D_lncont]LD.lngdppc = 0

( 3) [D_lncont]LD.lnimp = 0

( 2) [D_lncont]LD.lnexp = 0

( 1) [D_lncont]LD.lnnsc = 0

. test ([D_lncont]: LD.lnnsc LD.lnexp LD.lnimp LD.lngdppc)

Prob > chi2 = 0.2956

chi2( 5) = 6.11

( 5) [D_lncont]LD.lngdppc = 0

( 4) [D_lncont]LD.lnimp = 0

( 3) [D_lncont]LD.lnexp = 0

( 2) [D_lncont]LD.lnnsc = 0

( 1) [D_lncont]LD.lncont = 0

. test ([D_lncont]: LD.lncont LD.lnnsc LD.lnexp LD.lnimp LD.lngdppc)

Prob > chi2 = 0.0166

chi2( 1) = 5.74

( 1) [D_lncont]L._ce1 = 0

. test ([D_lncont]: L._ce1)



ISSN No:-2456-2165


explain the lncont. The chi2 (5) value is 6.11, and Prob> chi2 =

0.2956; that is the p-value is greater than 5 percent, so the null hypothesis cannot be rejected and it implies that there is no

short-run causality running from lnnsc, lnexp, lnimp, and

lngdppc to lncont.

G. Impulse Response Function The impulse responses of each variable to itself are

determined, and other variables die out after a specific

duration. A container throughput shock responds positively to

itself and other variables. The early response of container

throughput to a unit shock in ship call, export, import, and

GDP per capita is positive and considered as significant. It is

proven that the container throughput is caused by the number

of ship call, export, import, and GDP per capita Granger-

Cause. Rising container throughput may be viewed as having

a significant impact on the number of ship call, export, import,

and GDP per capita. The outcome shows that among the

variables, the impulse response of shock exists to each other and becomes extinct after a specific period, confirming the

constancy of the estimate model. The impulse response of

number of shipcall to container throughput, the impulse

response of export to container throughput, and the impulse

response of import to container throughput are observed, and

it is observed that the number of shipcall and value of export

and import enacts clearly than the result frequencies of ship,

schedule of shipping line of shipcall when increasing, GDP,

and population, which also denotes the increasing volume of

throughout may have significant impacts. Additionally, it is

observed that the impulse response of GDP per capita to container throughput is a shock of GDP to container

throughput that gives a clear response. Furthermore, the

impulse response of GDP per capita to container throughput is

a shock of GDP to container throughput that produces a clear

response.

After that, the initial shock response of container

throughput to export and GDP per capita is positive and can

be considered significant. It is simple to illustrate that

increasing export will increase GDP per capita, thereby

increasing total container throughput. Figure 4 also asserted

that an import shock responds negatively to itself and all other

variables, whereas container throughput responds more

positively than other variables. It suggests that increasing

import will have a greater impact on increasing container

throughput than other variables. As shown in Table 16 and 17,

the equilibrium adjustment process is relatively quick and leads to long-run equilibrium.

H. Post Diagnostics Check

Following the evaluation of the VECM model, the next step is to determine whether the appropriate model chosen

provides efficient data estimates and is well-specified.

Various post-estimation tests are performed to check for

misspecification and stability issues, as well as the problem of

serial correlation, among other things. All diagnostic checks

show that the model is stable, with no signs and symptoms of

autocorrelation, normality, or heteroskedasticity.

As a result, the model is well-designed and free of

any econometric issues and mistakes. The inverse roots of the

AR polynomial stability condition test display that no points

are outside the circle, indicating that the VECM model is

stable and the results are correct and valid. Based on the serial

correlation test results, the probability values show no

evidence of serial correlation among residuals. The null

hypothesis of no serial correlation is accepted at a 5 percent level of significance.The heteroskedasticity test

revealed that there is no heteroskedasticity in the model.

a) Stability Test

The VECM was tested for stability using an eigenvalue stability test, which revealed that the system is stable. K – r

unit eigenvalues are contained in the companion matrix of the

VECM with K endogenous variables and r cointegrating

equations. The moduli of the remaining r eigenvalues are

strictly less than one if the process is stable. It is difficult to

determine whether the eigenvalues' moduli are too close 1 and

similar because there is no general distribution theory for

moduli. The general rule in this test is that there would be a

sustainability problem if some of the remaining calculated

modules were very close to one. Eigenvalues appear in table

19 below. Only one of the eigenvalues is one, while the other

computed modules are not close to one.

The results show that there are about 0.338 of real roots.

There is no distribution theory that can be used to determine

how close this root is. Nonetheless, the root 0.339 supported earlier estimate concluded that the predicted

cointegration equation was most likely not stationary. The

eigenvalue graph reveals that the remaining eigenvalues are

not found near the unit circle. The stability check does not

indicate that the model was incorrectly specified. The graph

that accompanies figure 4 shows that all eigenvalues are

within the unit circle. The companion matrix has (5-1) unit

eigenvalues for a five-variable model with one cointegration

relationship. For stability, the remaining eigenvalue

coefficients must be exactly less than unity. Below, diagnostic

checks will be carried out on heteroskedasticity, the

autocorrelation of residuals, and normality.



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Table 19: Stability Test

b) LM Test of Autocorrelation

The Lagrange-Multiplier test is then used to check and

look for autocorrelation in the residuals of the VECM.

The null hypothesis of no autocorrection occurs at the optimal

lag selection. After evaluating the VECM model, we test

whether the residuals of this model are white noise or not. If

autocorrelation is observed between the residues, then it is implied that some information is left out of the model, such as

insufficient lags. The Lagrange-Multiplier (LM) test detects

serial correlation of residues up to a specified order of lag,

which should be the same as the corresponding VAR. The

result of the LM test at the 5 percent level is shown in Table

17. The null hypothesis, that there is no autocorrelation in the

residuals for any of the orders investigated, cannot be rejected.

As a result, no evidence of model misspecification can be

found in this test.

c) Multivariate Heteroskedasticity Test

Statistical evidence shows that the system is

homoskedastic or free of heteroskedasticity by checking the

Fig. 5: Stability Test

test in table 20 for the multivariate ARCH effect because the p-value under the null hypothesis is nonsignificant up to

the order of lagged 2.

d) Test for Normality of Residuals

Finally, an analysis of model disturbances is required. The test aims to check whether the residual has a normality

distribution or not. Jarque-Bera test, Kurtosis test,

andSkewness testcomputes series test statistics for H0, which

aid in confirming the VECM disturbances are normally

distributed. Failure to reject H0 indicates that the model is

incorrectly specified or misspecified. However, the lack of

residual normality does not render or cause the cointegration

tests and VECM invalid. The results of table 21, 22, and 23

shows that the model is not misspecified because null

hypothesis H0 is accepted. Thus the results regarding the

variables which have an effect on the container throughput could be accepted. Kurtosis test statistics do not reject H0 that

the terms of disturbance have kurtosis and skewness test

consistent with normality.

Table 19: Lagrange-Multiplier Test Table 20: Heteroskedasticity Test

.

The VECM specification imposes 4 unit moduli.

-.3340626 .334063

-.4764774 - .05195964i .479302

-.4764774 + .05195964i .479302

.6027718 .602772

.3384209 - .6716343i .752078

.3384209 + .6716343i .752078

1 1

1 1

1 1

1 1

Eigenvalue Modulus

Eigenvalue stability condition

. vecstable, graph

H0: no autocorrelation at lag order

2 21.6318 25 0.65691

1 21.5027 25 0.66427

lag chi2 df Prob > chi2

Lagrange-multiplier test

H0: no autocorrelation at lag order

2 21.6318 25 0.65691

1 21.5027 25 0.66427

lag chi2 df Prob > chi2

Lagrange-multiplier test



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Table 21: Jarque-Bera Test Table 22: Kurtosis Test

Table 23: Skewness Test


V. DISCUSSION

This study has discovered many interesting findings of

the factors affecting the maritime transport markets by using

applying the VECM model of time series econometric analysis to investigate the container

throughput of Cambodia's port and the problem that needs to

be taken action and solved for better improvement to

Cambodia's international ports to support the higher increases

of the container throughput traffics and demands in Cambodia

in long run plan and project of maritime transport as well as

strategy development of port and shipping market.The

findings of analysis and estimation in the also reveal many

interesting findings, which are discussed below;

A. Number of Ship Call and Container Throughput

The number of ship called has a significant and positive

impact on the combined container throughput of PAS and

PPAP. This result demonstrates that the flow of traffic

demand in ports explains Cambodia's port throughput and is

related to port performance and efficiency from an increase in number of ships that called at ports. It means that the port

authorities of PAS and PPAP are paying attention to and

focusing on the number of ship called at ports in order to meet

the increased demand for container throughput in ports,

because port performance is measured in terms of the number

of containers throughput. This is consistent with JICA's

master plan, which studied master plan ports in both PAS and

PPAS to have deep-sea port construction expand the large port

terminals and port facilities to support the higher container

throughput from the increased frequency of ship call in the

long run. The total number of ship call exhibits a positive sign

of association for statistically significant variables. It is positive for port throughput; ship call represent a lot of

opportunity and demand for the global shipping network, as

shown in Japan, indicating a congested condition at the port

level. The call frequency will help to support this

circumstance. The higher shipping frequency improves the

more efficiency of terminal operations. The researchers also

found that significant container ship call in Cambodia

improved container traffic throughput due to a significant

increase in the average volume carried by each ship. The

volume of containers passing through a terminal or port is

influenced by the number of ship call. If the number of ship

call is increased, it will be more appealing to exporters and

importers. Furthermore, suppose the port or terminal is

capable of providing higher quality and additional services to

commercial shipping. In that case, shippers will determine container cargo flows, thereby providing a competitive port.

Besides, liner shipping agreements and ship or vessel upsizing

strengthen the relationship between container shipping lines

and container terminals. As a result, shipping alliances can

choose to come to a port that will benefit them the most in

terms of deployed capacity, ports of call, network structure,

and port choice, and so on.

B. Import and Container Throughput

The findings result of import has a significant and negative

relationship with the container throughput of ports PAS and

PPAP. This result proves that Cambodia's port throughput in

the long term of the plan is explained by a decrease in imports

and is related to improved higher port efficiency, port

performance and terminal traffic efficiency. This negative

impact can be described as the fact that most imports or seaborne trade of imported goods from other countries' trading

partners to Cambodia will be reduced due to the high cost of

maritime shipping transportation and logistics and other

surcharges. Imported goods are becoming more expensive,

resulting in a decrease in import demand.The customs charge,

tariff, and port due are still the most significant impediments

to improving future import balance. As a result, other modes

of transport, such as land and air, which are less expensive

ALL 4.424 10 0.92618

D_lngdppc 0.610 2 0.73704

D_lnimp 0.450 2 0.79843

D_lnexp 0.906 2 0.63568

D_lnnsc 0.322 2 0.85139

D_lncont 2.136 2 0.34367

Equation chi2 df Prob > chi2

Jarque-Bera test

ALL 2.144 5 0.82891

D_lngdppc 2.5828 0.174 1 0.67654

D_lnimp 2.8556 0.021 1 0.88516

D_lnexp 2.6695 0.109 1 0.74101

D_lnnsc 2.5146 0.236 1 0.62739

D_lncont 4.2665 1.604 1 0.20534

Equation Kurtosis chi2 df Prob > chi2

Kurtosis test

ALL 2.281 5 0.80910

D_lngdppc .33022 0.436 1 0.50897

D_lnimp -.32763 0.429 1 0.51231

D_lnexp -.44634 0.797 1 0.37203

D_lnnsc -.14675 0.086 1 0.76914

D_lncont -.36473 0.532 1 0.46572

Equation Skewness chi2 df Prob > chi2

Skewness test



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than shipping by sea, will be phased out. The variable import

coefficient has a negative sign, indicating that Cambodia appeared to be diverting trade. It demonstrates a decline in

imports via shipping from ASEAN member countries or

trading partners. However, despite having a negative sign, the

estimated coefficient on import is significant, implying that

there is insufficient evidence that maritime transport shipping

of imported goods will affect Cambodia's imports in the

future. The existence of a direct relationship between import

and container throughput of international ports of Cambodia,

in other words, implied that a 1 percent increase in import

would lead to a negatively long-run effect on the performance

by 1.5 percent. This result contradicted the expected increase

in positively affected imports.

Several economic facilitation programs will be

implemented to address the economic problems of

international trade, the reduction and/or exemption of custom tariffs for imported goods, the compensation of tax and duty

paid on domestic goods for export, the reduction of import

duty for parts and spare parts for all types of automobiles,

including the reduction and/or exemption of income tax

related to international trade, and the reduction of the cost of

export production. This crucial policy framework and

mechanism will be promote and help define as the tools of

economic sector in order to measure the direct and indirect

impact of international trade promotion, and trade

exchanges.It should be noted that the above-mentioned

economic facilitation programs aim to lower a component of

import and export costs. The government, on the other hand, will be forced to increase national spending in the trade sector.

C. Export and Container Throughput

At the 5 percent level, the estimated coefficient export is

positive and statistically significant, indicating that export influences the demand for container throughput at the

combined Cambodian ports of PAS and PPAP. Export trade

volumes will rise in parallel with economic growth. Cambodia

is expected to move 4.1 times more products in 2030 than it

did in 2018. Maritime transport sectors and ports will increase

cargo and port throughput due to the increase in domestic

commodity exports. This significant and substantial

growth will require the development of both soft and hard

logistics infrastructure. Maritime transport policy is becoming

more efficient and effective to handle and process the

increased export volumes and increased export volumes and a wider range of import and export values. Cambodia must

improve its competitiveness to ensure that its exports-to-GDP

ratio remains at or near current levels in order to sustain

strongly significant economic growth in the medium and long

term. Cambodia should look into efficient and effective ways

to reduce logistics and supply chain bottlenecks in order to

improve service reliability and keep its international market

competitiveness. Because exports are satisfied with tariff-free

preferences in the markets of the US and EU, and are not

heavily influenced by ocean freight fees, rates, and

surcharges, reducing logistics costs, charges, and delays will

be the main strategic ways for Cambodia to further strengthen and maintain its high economic growth rates over the next 15

years.

Similarly, an increase in demand for imported goods by a

trading partner may result in higher demand created by an increase in the countries' exports. Some of the higher partners'

demand may increase Cambodia's exports, resulting in an

increase in maritime transport activities through Cambodia's

busiest ports, resulting in an increase in container throughput.

Export cargo is transported via the international trade ports of

PAS and PPAP. Some commodities are shipped by road from

or to Thailand, Laos, and Vietnam, as well as by air. Export

volume through both international ports has been increasing,

with the growth rate of containers through Phnom Penh Port

being particularly remarkable. The International distribution

ports will expand in the future, and export container and bulk

cargo will increase as a result of rational role sharing between land and sea transportation. PAS and PPAP have served as

international distribution centers in Cambodia and will

continue to do so in the future. Foreign trade through private

ports, on the other hand, is increasing, and private port

capacity is expanding rapidly. In the future, the private sector

intends to construct additional large ports. The roles of these

ports must be rationally assigned, taking into account the

unique characteristics of each port as well as the efficient use

of resources. Cambodian ports will improve their overall

functions through cooperation and competition among

themselves.

D. GDP Per capita and Container Throughput

GDP per capita has a negative sign and has a significant

impact on the demand for port throughput, which is

considered the port output and efficiency. In terms of the economic variable, container throughput and economic

growth are fundamentally linked with a tightening relationship

that is similar to China and generates a lot of container

throughput or port cargo like several other large developing

countries. Nonetheless, GDP per capita is lower than average,

resulting in a negative estimate. According to Vanoutrive,

Thomas (2010), a study linking economic activities and port

throughput in Belgium discovered very similar results that

demonstrated the correlation. The reason for this has already

been discussed, and it is due to Cambodia as a small country.

The relationship between port throughput and neighboring countries' GDP is frequently stronger than or equal to the

relationship between port throughput and the GDP of the

country in which the port is located.

More trade, particularly in intermediate products, is accompanied by regional or global supply chains, according to

Goldfarb and Beckman (2007); Robinson (2002); Goldfarb

and Thériault 2008). Because values are created at various

points along the supply chains, the relationship between what

is transported via container ship and port and its economic

impact is more complex. Musso et al. (2002) provides another

plausible explanation of outcome and result described how the

national economy was becoming increasingly irrelevant to

port success. This means that external economies, foreign

countries are becoming more important than national or local

economies. The effects of GDP per capita on port

performance or container throughput yields may be minor in the long run. The economic crisis, illness, or shock will all

have a greater impact, and GDP per capita has even become

negatively and significantly related on the demand for



ISSN No:-2456-2165


throughput of ports. According to Wildenboer, E. (2015)

shown that when per capita GDP rises by 1000, container falls by 451,608 TEU. This parameter has a large negative impact

on container throughput. This is a remarkable discovery

because a significant positive relationship between per capita

income and national GDP on the one hand, and port

performance on the other, can be predicted.

VI. CONCLUSION

Based on the estimated results and discussions, it is

possible to conclude that the endogenous variable was container throughput for Cambodia's international ports,

namely the PAS and PPAP ports. Simultaneously, the

exogenous variables are the number of shipcall, export,

import, and GDP per capita, which are all in natural

logarithms. To estimate research work, the cointegration

equation and the VECM are used. Meanwhile, it is discovered

that the series used in this analysis is nonstationary at the level

but stationary at the first differencing.A sequential likelihood

ratio test was performed on the rank of the estimated

parameter matrix from the VECM model to identify the

number of cointegration relations. Recently, the IRF was used to determine whether or not there is any shock in the response

of one factor to another.The empirical analysis of this study

reveals the best fit of the model by developing the long-run

equilibrium relationship advancement of the actual data time

series data, as evidenced by appropriate estimation findings of

results. The study also further examines the long-run causal

relationships among the variables influencing container

throughput in time series data. The statistical and econometric

evidence revealed that the estimating series has a statistically

and significantly long-run equilibrium relationship, which

affects Cambodia's overall container throughput.

Besides that, it is critical to emphasize that the VECM

results are not based on the short-run granger-causality

relationship that runs from GDP per capita, ship call, import

and export, to container throughput. According to the estimation results of the IRF test, the shock response of

independent variables to container throughput disappears after

a while. Furthermore, the container throughput at Cambodia's

ports generated by VECM yielded satisfactory results.The

model was appropriate, and other methods of measuring tests

were considered in order to evaluate the model's goodness and

accuracy. The PP and ADF results for unit root tests

demonstrate that all series are nonstationary at the level but

stationary in the first differencing, i.e. they are integrated with

the order one I (1). The research then keeps moving on to

examine the long-run equilibrium relationship among

exogenous variables using Johansen's multivariate cointegration test. The findings revealed the presence of a

cointegration vector between these variables. Various

diagnostic and post-estimation tests are performed to

investigate misspecification issues as well as the model's

stability and robustness in order to obtain the best possible

results and correctly confirm them. The stability check shows

that our model is not misspecified or incorrectly

specified. Based on the Lagrange-Multiplier for LM test

results, we cannot reject the null hypothesis, which states that

there is no serial correlation or autocorrelation the residuals

for any of the orders studied. As a result, the results of the

tests show that there is no evidence of model misspecification.

Our model does not have misspecification, according to Jarque-Bera, Kurtosis, and Skewness test results. For all

equations, the null hypothesis for the normality of distributed

convergence to recover the long-run equilibrium position is

not rejected. As a result, the model is satisfied and appropriate

in the research and analysis. As a matter of conclusion, it is

possible to conclude that the number of ship call has a

statistically and significantly positive effect on container

throughput. The coefficient of a number of ship call is

1.82213, indicating that it is more elastic (greater than 1). It

shows that a 100 percent rise in the number of ship called is

likely to increase container throughput by 182.22 percent in

the long run in a positive relationship to Cambodia's port throughput. An rise in the number of ships calling at

Cambodian ports would boost maritime sector activities such

as loaded or unloaded cargo transportation and the frequency

of shipping traffic at the ports.As a result, container

throughput would increase. Export has a statistically

significant impact on container throughput and has a positive

effect on Cambodian container throughput.The coefficient

value is explained by 1.00068, which is greater than 1 and is

considered elastic. When exports increase by 100 percent,

container throughput increases by more than 100.068 percent.

As a result, Cambodia should prioritize export promotion policies in order to increase seaborne trade and export cargoes

of final products from its countries, allowing maritime time

transport activities to become more interesting and busy as the

tools and measure in TEUs of container put throughput at

port.

The findings of import also indicated and thus confirmed

a long-run negative relationship between import and container

throughput. The coefficient value is produced by minus

1.51536, which is greater than 1 of absolute value and

considered elastic, implying that a 100 percent increase in

import will reduce the impact of nearly 151.53 percent of

container throughput long-run negative relationship. The

findings revealed a long-run negative relationship between

import and demand for container throughput because imports

of goods are valued at their free on board market prices at foreign ports, which do not include Cambodian import duties,

ocean freight, maritime transport services such as insurance,

travel expense and maritime transport freight paid to foreign

carriers or firm that need to be shipped because imported

goods are not produced in Cambodia, so the cost will be more

expensive.

Also, GDP per capita and container throughput have a

significant effect on and a negative long-run relationship with

each other in line with the studies conducted by OECD (2014)

on time efficiency at world port container,. The value of the

coefficient is explained by minus 1.00532, which is greater

than 1 and is considered elastic in absolute value. The result

indicated a 100 percent increase in GDP per capita; however,

with a negative long run relationship, container throughput is

reduced by more than 100.53 percent. This can lead to the

conclusion that international economies and the hinterland, neighboring countries, or the rest of the world are given more

weight than national or local economies. This demonstrates

that the national economy is becoming less and less essential



ISSN No:-2456-2165


for port performance as a result of the economic crisis,

random shocks, and diseases in the country, such as SARs in Hong Kong and Covid-19 of the global pandemic, all of

which reduce a country's GDP and GDP per capita. Despite

this, the higher container port cargo throughput was still

significantly higher.With the best results, it can now conclude

that the coefficient values are all elastic. As a result, it is

concluded that container throughput is relatively more elastic

in terms of the number of ship call than in terms of import,

export, and GDP per capita, and there are also long-run

equilibrium relationships. In the short run, the granger

causality test revealed no causal relationship among container

throughput and exogenous variables like GDP per capita

import and export, and ship call.

In the short run, the granger causality test revealed no

causal relationship between container throughput and

exogenous variables like GDP per capita import and export, and ship call. This finding was consistently responsible for the

long analysis, which is explained by the fact that

the joint variables of GDP per capita, number of ship call,

import, and export do not granger-cause container throughput

is rejected.

VII. POLICY IMPLICATION AND RECOMMENDATION

Cambodia's government ports, MPWT, and port

authority should develop, design, and implement the best port policy and strategy planning, such as developing future

terminals, infrastructure, container ports, and facilities, in

order to provide better maritime transport service, quality port

services, and shippers and liner shipping

companies.Moreover, due to the capacity of the existing

container port, the building of new ports to meet the future

demand will increasingly be unable to handle such massive

container demand (Syafi'i et al., 2005; JICA, 2007).The

government and MPWT should consider developing and

formulating a maritime transport strategic policy to promote

long-term economic growth and sustainability. The findings of this study suggested that port authorities may have to make

all necessary decisions to invest in developing infrastructures

of ports and port facilities, transportation, container terminals,

and logistic connectivity, among other things, because

increased productivity of port facilities will lead to increased

demand for container throughput or volume of throughput in

the future.Port capacity must be increased in order to meet and

forecast rising demand. As a result, the robust analysis and

estimated results of the findings found in this research greatly

assist the government, MPWT, and port authorities in making

better decisions that could provide appropriate values for

meeting the increasing demand of throughput of Cambodian ports and investment, finance, and operation in the future to

handle and sustain the higher demand of throughput.

VIII. DECLARATION

The author declares that there are no conflicts of interest

in the publication of this paper. This research is not supported

by any external funding or grants from public, non-profit, or

commercial organizations. Errors and omissions, if any, are solely the author's responsibility.

IX. ACKNOWLEDGEMENT

The author wishes to thank the anonymous peer-

reviewers and editors for their insightful comments, which

helped improve this scientific journal from its original form,

as well as Dr. Kang Sovannara, Dr. NhemSareth, and Dr. Kim

ChomroeunVuthy. My heartfelt gratitude goes to Southern Denmark University and the MERE group scholars, together

with Dr. NielsVestergaard, Dr. Eva Roth, Dr. Brooks Kaiser,

Dr. Melina Kourantidou, and Dr. Lars RavnJonsen.

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